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   "cell_type": "markdown",
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    "id": "nc0g2NLpUSGr"
   },
   "source": [
    "# Fine-tune IDEFICS3 on Visual Question Answering\n",
    "\n",
    "In this notebook we will fine-tune IDEFICS3 on VQAv2 dataset.\n",
    "\n",
    "The transformers PR isn't merged yet so we will install the branch that contains the transformers implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "WIhA1lQ7j0kw",
    "outputId": "75d422a4-e258-455d-9b48-9fba36c060c3"
   },
   "outputs": [],
   "source": [
    "!pip install -q accelerate datasets peft bitsandbytes transformers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "WIhA1lQ7j0kw",
    "outputId": "75d422a4-e258-455d-9b48-9fba36c060c3"
   },
   "outputs": [],
   "source": [
    "!pip install -q flash-attn --no-build-isolation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wAeMA0heVBjT"
   },
   "source": [
    "We will push out model to Hub so we need to authenticate ourselves."
   ]
  },
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    "id": "yKd5xtSGj7cm",
    "outputId": "ca0d369a-8e70-46a6-bb77-50ed3509ab39"
   },
   "outputs": [],
   "source": [
    "from huggingface_hub import notebook_login\n",
    "\n",
    "notebook_login()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WRq8ve-LVAzU"
   },
   "source": [
    "In this notebook we will not do full fine-tuning but use QLoRA method, which loads an adapter to the quantized version of the model, saving space. If you want to do full fine-tuning, set `USE_LORA` and `USE_QLORA` to False. If you want to do LoRA, set `USE_QLORA` to False and `USE_LORA` to True."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ[\"CUDA_DEVICE_ORDER\"] = \"PCI_BUS_ID\"\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"4\" # you don't need this unless you work on a multigpu setup and need to use a specific index\n",
    "# if you want to use multiple GPUs, use e.g. \"2,4\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "for param in model.model.vision_model.parameters():\n",
    "    param.requires_grad = False "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QtjggkcTVnSV"
   },
   "source": [
    "We will load VQAv2 dataset. For educational purposes we will load the validation split and split it twice."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "POOqKqYRka5O",
    "outputId": "87977922-2c3a-4c96-fffb-7c097b0815fa"
   },
   "outputs": [],
   "source": [
    "from datasets import load_dataset\n",
    "ds = load_dataset('merve/vqav2-small', trust_remote_code=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "Znf9vMo5rnSd"
   },
   "outputs": [],
   "source": [
    "split_ds = ds[\"validation\"].train_test_split(test_size=0.8)\n",
    "train_ds = split_ds[\"train\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset({\n",
       "    features: ['multiple_choice_answer', 'question', 'image'],\n",
       "    num_rows: 4287\n",
       "})"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from peft import LoraConfig, prepare_model_for_kbit_training, get_peft_model\n",
    "from transformers import AutoProcessor, BitsAndBytesConfig, Idefics3ForConditionalGeneration\n",
    "\n",
    "USE_LORA = False\n",
    "USE_QLORA = False\n",
    "model_id = \"HuggingFaceM4/Idefics3-8B-Llama3\"\n",
    "\n",
    "processor = AutoProcessor.from_pretrained(\n",
    "    model_id\n",
    ")\n",
    "\n",
    "if USE_QLORA or USE_LORA:\n",
    "    lora_config = LoraConfig(\n",
    "        r=8,\n",
    "        lora_alpha=8,\n",
    "        lora_dropout=0.1,\n",
    "        target_modules=['down_proj','o_proj','k_proj','q_proj','gate_proj','up_proj','v_proj'],\n",
    "        use_dora=False if USE_QLORA else True,\n",
    "        init_lora_weights=\"gaussian\"\n",
    "    )\n",
    "    lora_config.inference_mode = False\n",
    "    if USE_QLORA:\n",
    "        bnb_config = BitsAndBytesConfig(\n",
    "            load_in_4bit=True,\n",
    "            bnb_4bit_use_double_quant=True,\n",
    "            bnb_4bit_quant_type=\"nf4\",\n",
    "            bnb_4bit_compute_dtype=torch.bfloat16\n",
    "        )\n",
    "        \n",
    "    model = Idefics3ForConditionalGeneration.from_pretrained(\n",
    "        model_id,\n",
    "        quantization_config=bnb_config if USE_QLORA else None,\n",
    "        _attn_implementation=\"flash_attention_2\",\n",
    "        device_map=\"auto\"\n",
    "    )\n",
    "    model.add_adapter(lora_config)\n",
    "    model.enable_adapters()\n",
    "    model = prepare_model_for_kbit_training(model)\n",
    "    model = get_peft_model(model, lora_config)\n",
    "    print(model.get_nb_trainable_parameters())\n",
    "else:\n",
    "    model = Idefics3ForConditionalGeneration.from_pretrained(\n",
    "        model_id,\n",
    "        torch_dtype=torch.bfloat16,\n",
    "        _attn_implementation=\"flash_attention_2\",\n",
    "    ).to(DEVICE)\n",
    "    \n",
    "    # if you'd like to only fine-tune LLM\n",
    "    for param in model.model.vision_model.parameters():\n",
    "        param.requires_grad = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5nwMO3n0X7Hv"
   },
   "source": [
    "Let's write our data collating function. We will apply prompt template to have questions and answers together so model can learn to answer. Then we pass the formatted prompts and images to the processor which processes both."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "e0krVLZ-wNMl"
   },
   "outputs": [],
   "source": [
    "image_token_id = processor.tokenizer.additional_special_tokens_ids[\n",
    "            processor.tokenizer.additional_special_tokens.index(\"<image>\")]\n",
    "\n",
    "def collate_fn(examples):\n",
    "  texts = []\n",
    "  images = []\n",
    "  for example in examples:\n",
    "      image = example[\"image\"]\n",
    "      question = example[\"question\"]\n",
    "      answer = example[\"multiple_choice_answer\"]\n",
    "      messages = [\n",
    "          {\n",
    "              \"role\": \"user\",\n",
    "              \"content\": [\n",
    "                  {\"type\": \"text\", \"text\": \"Answer briefly.\"},\n",
    "                  {\"type\": \"image\"},\n",
    "                  {\"type\": \"text\", \"text\": question}\n",
    "              ]\n",
    "          },\n",
    "          {\n",
    "              \"role\": \"assistant\",\n",
    "              \"content\": [\n",
    "                  {\"type\": \"text\", \"text\": answer}\n",
    "              ]\n",
    "          }\n",
    "      ]\n",
    "      text = processor.apply_chat_template(messages, add_generation_prompt=False)\n",
    "      texts.append(text.strip())\n",
    "      images.append([image])\n",
    "\n",
    "  batch = processor(text=texts, images=images, return_tensors=\"pt\", padding=True)\n",
    "  labels = batch[\"input_ids\"].clone()\n",
    "  labels[labels == processor.tokenizer.pad_token_id] = -100\n",
    "  labels[labels == image_token_id] = -100 \n",
    "  batch[\"labels\"] = labels\n",
    "\n",
    "  return batch\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QvAs896cdwg8"
   },
   "source": [
    "We can now initialize `Trainer` and initialize `TrainingArguments` to pass to `Trainer`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "QNE2yWAYrAhD",
    "outputId": "2bdefa08-a54b-40e0-cae8-f029ff8312e7"
   },
   "outputs": [],
   "source": [
    "from transformers import TrainingArguments, Trainer\n",
    "\n",
    "training_args = TrainingArguments(\n",
    "    num_train_epochs=1,\n",
    "    per_device_train_batch_size=2,\n",
    "    gradient_accumulation_steps=8,\n",
    "    warmup_steps=50,\n",
    "    learning_rate=1e-4,\n",
    "    weight_decay=0.01,\n",
    "    logging_steps=25,\n",
    "    save_strategy=\"steps\",\n",
    "    save_steps=250,\n",
    "    save_total_limit=1,\n",
    "    optim=\"adamw_hf\", # for 8-bit, pick paged_adamw_hf\n",
    "    #evaluation_strategy=\"epoch\",\n",
    "    bf16=True,\n",
    "    output_dir=\"./idefics3-llama-vqav2\",\n",
    "    hub_model_id=\"idefics3-llama-vqav2\",\n",
    "    remove_unused_columns=False,\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainer = Trainer(\n",
    "    model=model,\n",
    "    args=training_args,\n",
    "    data_collator=collate_fn,\n",
    "    train_dataset=train_ds,\n",
    "    #eval_dataset=test_ds,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I'm running standalone scripts on top of tmux so the logs will not appear here. I will upload my training script to this repository."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainer.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainer.push_to_hub()"
   ]
  }
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